AI Skills

🔅 02 - Ingredients of an LLM

🔅 01 - Introduction to LLMs

LLMs Intuition 01 - Introduction to LLMs 02 - Ingredients of an LLM 03 - Who invented LLMs 04 - How LLMs generate text 05 - Inside an LLM Under the Hood 06 - LLM Parameters 07 - LLM Context Window 08 - FineTuning LLMs

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📖 7 must-know strategies to scale your database. 1 - Indexing: Check the query patterns of your application and create the right indexes. 2 - Materialized Views: Pre-compute complex query results and store them for faster access. 3 - Denormalization: Reduce complex joins to improve query performance. 4 - Vertical Scaling Boost your database server by adding more CPU, RAM, or storage. 5 - Caching Store frequently accessed data in a faster storage layer to reduce database load. 6 - Replication Create replicas of your primary database on different servers for scaling the reads. 7 - Sharding Split your database tables into smaller pieces and spread them across servers. Used for scaling the writes as well as the reads.

3. High Performance: Despite its smaller size, LLaVA delivers fast and accurate results in vision-related tasks. The instructor demonstrates how LLaVA can quickly analyze and describe images, often faster than a human could interpret the same visual information. 💡 Running LLaVA with Llamafile: The instructor uses Llamafile, a tool that packages large language models into a single binary file, to run LLaVA locally. Llamafile simplifies the process of deploying and running models like LLaVA, making it accessible for users who want to experiment with local AI models. 💡 Advantages of Small, Specialized Models: 1. Task-Specific Optimization: LLaVA is optimized for computer vision tasks, allowing it to perform these tasks more efficiently than general-purpose models. This specialization leads to faster performance and better accuracy in its domain. 2. Accessibility Applications: The instructor suggests that LLaVA could be particularly useful for accessibility applications, such as generating alt text for images in educational courses or other workflows. This makes it a valuable tool for developers and educators who need to create accessible content. 3. Local Execution: Running LLaVA locally with Llamafile ensures privacy and low latency, as the data does not need to be sent to external servers. This makes it ideal for applications where data security and real-time performance are important. 💡 Conclusion: LLaVA is a powerful example of how small, specialized language models can excel in specific tasks, such as multi-modal vision and language understanding. Its ability to quickly and accurately describe images makes it a valuable tool for applications like accessibility, education, and content creation. By using tools like Llamafile, users can easily deploy and run LLaVA locally, benefiting from its efficiency, speed, and privacy. The instructor encourages viewers to explore LLaVA and consider its potential for specialized AI applications

🔗 04. Demo of LLaVA - A Small Language Model for Vision and Language Tasks LLaVA (Large Language and Vision Assistant) is a small language model specifically designed for multi-modal vision and language tasks. LLaVA is an example of how smaller, specialized models can outperform larger models in specific domains, particularly in computer vision and image understanding. 💡 Key Features of LLaVA: 1. Specialized for Vision and Language: LLaVA is optimized for multi-modal tasks, meaning it can process both text and images. This makes it particularly useful for applications that require understanding and describing visual content, such as image captioning or accessibility tools. 2. Small and Efficient: Although LLaVA is larger than some other small models, it is still significantly smaller than traditional large language models (e.g., 30 GB models). Its compact size allows it to run efficiently on local hardware, such as a Mac with an M Series chip, without requiring extensive computational resources.

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How to Learn Backend Development?

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Maths for Machine Learning 👆

7. Define the main function to run all LLMs and aggregate results. • Runs all reference models asynchronously • Displays individual responses in expandable sections • Aggregates responses using the aggregator model • Streams the aggregated response.

6. Implement the LLM call function. • Asynchronously calls the LLM with the user's prompt • Returns the model name and its response

5. Define the models and aggregator system prompt. • Specifies the LLMs to be used for generating responses • Defines the aggregator model and its system prompt

4. Initialize Together AI clients. • Sets up Together API key as an environment variable • Initializes both synchronous and asynchronous Together clients